1 citations · 2 across the 3 of their papers we have counts for
Showing cs.DCShow all
3 papers · 1 filter
cs.DC2026
HiNa-MoE: High-Performance, Non-Intrusive MoE Inference on CPUs with Matrix Engines
Weiling Yang, Junwen Zhang, Dezun Dong +4
Mixture-of-Experts (MoE) inference is increasingly deployed in local and on-premise environments, where expert parameters often exceed GPU memory capacity. In latency-sensitive, lo…
cs.DC2026
Physically Partitioned KVCache Format for CPU--GPU Load Balancing in MoE Inference
Enda Yu, Dezun Dong, Xiangke Liao
Single-GPU long-context inference with Mixture-of-Experts (MoE) models requires spilling the key-value cache (KVCache) to CPU memory. The spilled KV serves two complementary purpos…
cs.DC2024
Nezha: Breaking Multi-Rail Network Barriers for Distributed DNN Training
Enda Yu, Dezun Dong, Xiangke Liao
In distributed deep learning, communication remains a critical bottleneck. While modern hardware advances rapidly, over 60 percent of production HPC systems still rely on legacy in…